Computing Resources Scalability Performance Analysis in Cloud Computing Data Center

Today, cloud computing has become an essential technology in modern times, offering a wide range of benefits to organizations of all sizes. It provides access to computing resources on-demand over the internet, reducing costs and enabling organizations to respond quickly to changing business needs. Dynamic scalability is a crucial feature of cloud computing, allowing the system to dynamically allocate resources based on user demand at runtime while providing high quality of service (QoS) and performance to clients with minimal resource usage. This paper proposes a stochastic model based on queueing theory to study and analyze the performance of cloud data centers (CDC) and meet service level agreements (SLA) established with clients. The model is used to examine various performance metrics, including the mean response time, the mean waiting time, the probability of rejection, and the utilization of the system, as the arrival rate and the service rate vary. Simulation results are provided using the CloudSim simulator. The results of the analysis and simulation show that our model accurately estimates the number of virtual machines (VMs) required to meet QoS objectives, making it a valuable tool for improving the performance and scalability of cloud data centers. The results obtained from our analytical model are validated by an experimental example conducted on the Amazon Web Services (AWS) cloud platform.